{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    " *需安装numpy，pandas，scipy，detecta，matplotlib.pyplot，sklearn*\n",
    "### raw data读取与预处理\n",
    "+ mid5是窗口为5的中值滤波\n",
    "+ 小程序自身采样率已经不足20Hz，故在此省去低通滤波，低通滤波只用于提取重力加速度\n",
    "+ 取窗口2s，print窗口信号个数"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "38\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "from scipy import signal\n",
    "\n",
    "\n",
    "#中值滤波\n",
    "def mid5(arr):\n",
    "    arr=[0,0]+list(arr)+[0,0]\n",
    "    output=[]\n",
    "    # print(len(arr))\n",
    "    for i in range(2,len(arr)-2):\n",
    "        temp=list(arr[i-2:i+3])\n",
    "        # print(temp)\n",
    "        temp.sort()\n",
    "        output.append(temp[2])\n",
    "    # print(output)\n",
    "    return np.array(output)\n",
    "\n",
    "\n",
    "def prefilter(x):\n",
    "    x=mid5(x)\n",
    "    # b, a = signal.butter(3, 0.99, 'lowpass')    # changeable\n",
    "    # x=signal.filtfilt(b, a, x)\n",
    "    return np.array(x, dtype=np.float32)\n",
    "\n",
    "def window(x,block):\n",
    "    win=[]\n",
    "    for i in range(block*2,len(x),block):\n",
    "        win.append(np.array([j for j in x[i-block*2:i]]))\n",
    "    return np.array(win)\n",
    "\n",
    "\n",
    "def prepro(path,time):\n",
    "    data = pd.read_csv(path)\n",
    "    n,m=data.shape\n",
    "    freq=n/time\n",
    "\n",
    "    taccX=prefilter(data['accX'])\n",
    "    taccY=prefilter(data['accY'])\n",
    "    taccZ=prefilter(data['accZ'])\n",
    "    gyrX=prefilter(data['geoX'])\n",
    "    gyrY=prefilter(data['geoY'])\n",
    "    gyrZ=prefilter(data['geoZ'])\n",
    "\n",
    "    ctf=0.3\n",
    "    # signal.butter(3,2*ctf/freq,'low')\n",
    "    b, a = signal.butter(3, 2*ctf/freq, 'low')   # changeable\n",
    "    baccX = np.array(signal.filtfilt(b, a, taccX))\n",
    "    b, a = signal.butter(3, 2*ctf/freq, 'low')   # changeable\n",
    "    baccY = np.array(signal.filtfilt(b, a, taccY))\n",
    "    b, a = signal.butter(3, 2*ctf/freq, 'low')   # changeable\n",
    "    baccZ = np.array(signal.filtfilt(b, a, taccZ))\n",
    "\n",
    "    block=int(2*freq//2+1 )    # changeable\n",
    "    # block=int(2*block)\n",
    "    \n",
    "\n",
    "    taccX=window(taccX,block)\n",
    "    taccY=window(taccY,block)\n",
    "    taccZ=window(taccZ,block)\n",
    "    baccX=window(baccX,block)\n",
    "    baccY=window(baccY,block)\n",
    "    baccZ=window(baccZ,block)\n",
    "    gyrX=window(gyrX,block)\n",
    "    gyrY=window(gyrY,block)\n",
    "    gyrZ=window(gyrZ,block)\n",
    "\n",
    "    signals=[baccX,baccY,baccZ,gyrX,gyrY,gyrZ,taccX,taccY,taccZ]\n",
    "    signals = np.transpose(np.array(signals), (1, 2, 0))\n",
    "\n",
    "    label = window(data['label'], block)\n",
    "    label = np.array([max(x, key=list(x).count) for x in label])\n",
    "\n",
    "    return signals,label,2*block,freq,1/block\n",
    "\n",
    "\n",
    "train_signals,train_labels,trN,trF_s,trT=prepro('./dataset/HARtrain480s.csv',480)\n",
    "test_signals,test_labels,tsN,tsF_s,tsT=prepro('./dataset/HARtest80s.csv',80)\n",
    "print(len(train_signals[0]))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 特征提取与可视化\n",
    "+ 分别用fft，psd，auto-correlation变换信号后再进行特征提取\n",
    "+ 特征包括9个信号每种变换前5个峰值的x和y值(9\\*3\\*5*2)\n",
    "+ 对同一窗口下的预处理信号及变换信号可视化\n",
    "+ 根据可视化的观察选取某个动作合适的计数信号，即选取波峰显著周期明显的信号以进行波峰计数\n",
    "+ 更改signal_no查看不同窗口的可视化结果"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stderr",
     "text": [
      "D:\\software\\Programming\\anaconda\\lib\\site-packages\\scipy\\signal\\spectral.py:1966: UserWarning: nperseg = 256 is greater than input length  = 38, using nperseg = 38\n  .format(nperseg, input_length))\nD:\\software\\Programming\\anaconda\\lib\\site-packages\\scipy\\signal\\spectral.py:1966: UserWarning: nperseg = 256 is greater than input length  = 38, using nperseg = 38\n  .format(nperseg, input_length))\nD:\\software\\Programming\\anaconda\\lib\\site-packages\\scipy\\signal\\spectral.py:1966: UserWarning: nperseg = 256 is greater than input length  = 38, using nperseg = 38\n  .format(nperseg, input_length))\nD:\\software\\Programming\\anaconda\\lib\\site-packages\\scipy\\signal\\spectral.py:1966: UserWarning: nperseg = 256 is greater than input length  = 38, using nperseg = 38\n  .format(nperseg, input_length))\nD:\\software\\Programming\\anaconda\\lib\\site-packages\\scipy\\signal\\spectral.py:1966: UserWarning: nperseg = 256 is greater than input length  = 38, using nperseg = 38\n  .format(nperseg, input_length))\nD:\\software\\Programming\\anaconda\\lib\\site-packages\\scipy\\signal\\spectral.py:1966: UserWarning: nperseg = 256 is greater than input length  = 38, using nperseg = 38\n  .format(nperseg, input_length))\nD:\\software\\Programming\\anaconda\\lib\\site-packages\\scipy\\signal\\spectral.py:1966: UserWarning: nperseg = 256 is greater than input length  = 38, using nperseg = 38\n  .format(nperseg, input_length))\nD:\\software\\Programming\\anaconda\\lib\\site-packages\\scipy\\signal\\spectral.py:1966: UserWarning: nperseg = 256 is greater than input length  = 38, using nperseg = 38\n  .format(nperseg, input_length))\nD:\\software\\Programming\\anaconda\\lib\\site-packages\\scipy\\signal\\spectral.py:1966: UserWarning: nperseg = 256 is greater than input length  = 38, using nperseg = 38\n  .format(nperseg, input_length))\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 864x864 with 12 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.fftpack import fft\n",
    "from scipy.signal import welch\n",
    "from detecta import detect_peaks\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "signal_no = 136\n",
    "\n",
    "def get_fft_values(y_values, T, N, f_s):\n",
    "    f_values = np.linspace(0.0, 1.0 / (2.0 * T), N // 2)    # 频值x\n",
    "    fft_values_ = fft(y_values)\n",
    "    fft_values = 2.0 / N * np.abs(fft_values_[0:N // 2])    # 幅值y\n",
    "    return f_values, fft_values\n",
    "\n",
    "def get_psd_values(y_values, T, N, f_s):\n",
    "    f_values, psd_values = welch(y_values, fs=f_s)\n",
    "    return f_values, psd_values\n",
    "\n",
    "\n",
    "def autocorr(x):\n",
    "    result = np.correlate(x, x, mode='full')\n",
    "    return result[len(result) // 2:]\n",
    "\n",
    "\n",
    "def get_autocorr_values(y_values, T, N, f_s):\n",
    "    autocorr_values = autocorr(y_values)\n",
    "    x_values = np.array([T * jj for jj in range(0, N)])\n",
    "    return x_values, autocorr_values\n",
    "\n",
    "\n",
    "def get_first_n_peaks(x, y, no_peaks=5):\n",
    "    x_, y_ = list(x), list(y)\n",
    "    if len(x_) >= no_peaks:\n",
    "        return x_[:no_peaks], y_[:no_peaks]\n",
    "    else:\n",
    "        missing_no_peaks = no_peaks - len(x_)\n",
    "        return x_ + [0] * missing_no_peaks, y_ + [0] * missing_no_peaks\n",
    "\n",
    "\n",
    "def get_features(x_values, y_values, mph):\n",
    "    indices_peaks = detect_peaks(y_values, mph=mph)\n",
    "    peaks_x, peaks_y = get_first_n_peaks(x_values[indices_peaks], y_values[indices_peaks])\n",
    "    return peaks_x + peaks_y\n",
    "\n",
    "\n",
    "def extract_features_labels(dataset, labels, T, N, f_s, denominator):\n",
    "    percentile = 5\n",
    "    list_of_features = []\n",
    "    list_of_labels = []\n",
    "    for signal_no in range(0, len(dataset)):\n",
    "        features = []\n",
    "        list_of_labels.append(labels[signal_no])\n",
    "        for signal_comp in range(0, dataset.shape[2]):\n",
    "            signal = dataset[signal_no, :, signal_comp]\n",
    "\n",
    "            signal_min = np.nanpercentile(signal, percentile)\n",
    "            signal_max = np.nanpercentile(signal, 100 - percentile)\n",
    "            # ijk = (100 - 2*percentile)/10\n",
    "            mph = signal_min + (signal_max - signal_min) / denominator\n",
    "\n",
    "            features += get_features(*get_psd_values(signal, T, N, f_s), mph)\n",
    "            features += get_features(*get_fft_values(signal, T, N, f_s), mph)\n",
    "            features += get_features(*get_autocorr_values(signal, T, N, f_s), mph)\n",
    "        list_of_features.append(features)\n",
    "    return np.array(list_of_features), np.array(list_of_labels)\n",
    "\n",
    "\n",
    "def get_values(y_values, T, N, f_s):\n",
    "    y_values = y_values\n",
    "    x_values = [19 * kk for kk in range(0, len(y_values))]\n",
    "    return x_values, y_values\n",
    "\n",
    "\n",
    "####\n",
    "\n",
    "labels = ['x-component', 'y-component', 'z-component']\n",
    "colors = ['r', 'g', 'b']\n",
    "suptitle = \"Different signals for the activity: {}\"\n",
    "\n",
    "xlabels = ['Time [sec]', 'Freq [Hz]', 'Freq [Hz]', 'Time lag [s]']\n",
    "ylabel = 'Amplitude'\n",
    "axtitles = [['Acceleration', 'Gyro', 'Total acceleration'],\n",
    "            ['FFT acc', 'FFT gyro', 'FFT total acc'],\n",
    "            ['PSD acc', 'PSD gyro', 'PSD total acc'],\n",
    "            ['Autocorr acc', 'Autocorr gyro', 'Autocorr total acc']\n",
    "            ]\n",
    "activities_description=['0','1','2','3']\n",
    "list_functions = [get_values, get_fft_values, get_psd_values, get_autocorr_values]\n",
    "\n",
    "N = 38\n",
    "f_s = 19\n",
    "t_n = 2\n",
    "T = t_n / N\n",
    "\n",
    "signals = train_signals[signal_no, :, :]\n",
    "# print(signals.shape)\n",
    "label = train_labels[signal_no]\n",
    "activity_name = activities_description[label]\n",
    "# activity_name =label\n",
    "f, axarr = plt.subplots(nrows=4, ncols=3, figsize=(12, 12))\n",
    "f.suptitle(suptitle.format(activity_name), fontsize=16)\n",
    "\n",
    "for row_no in range(0, 4):\n",
    "    for comp_no in range(0, 9):\n",
    "        col_no = comp_no // 3\n",
    "        plot_no = comp_no % 3\n",
    "        color = colors[plot_no]\n",
    "        label = labels[plot_no]\n",
    "\n",
    "        axtitle = axtitles[row_no][col_no]\n",
    "        xlabel = xlabels[row_no]\n",
    "        value_retriever = list_functions[row_no]\n",
    "\n",
    "        ax = axarr[row_no, col_no]\n",
    "        ax.set_title(axtitle, fontsize=16)\n",
    "        ax.set_xlabel(xlabel, fontsize=16)\n",
    "        if col_no == 0:\n",
    "            ax.set_ylabel(ylabel, fontsize=16)\n",
    "\n",
    "        signal_component = signals[:, comp_no]\n",
    "        x_values, y_values = value_retriever(signal_component, T, N, f_s)\n",
    "        ax.plot(x_values, y_values, linestyle='-', color=color, label=label)\n",
    "        if row_no > 0:\n",
    "            max_peak_height = 0.1 * np.nanmax(y_values)\n",
    "            indices_peaks = detect_peaks(y_values, mph=max_peak_height)\n",
    "            ax.scatter(x_values[indices_peaks], y_values[indices_peaks], c=color, marker='*', s=60)\n",
    "            # print('peak freq',x_values[indices_peaks])\n",
    "        if col_no == 2:\n",
    "            ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(top=0.90, hspace=0.6)\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 模型训练与评估\n",
    "+ 比较模型SVM，RF，朴素贝叶斯\n",
    "+ 比较特征提取训练集与滤波6轴训练集\n",
    "+ 发现3种模型2种训练集效果都很好\n",
    "+ 最终选用较简单的朴素贝叶斯，滤波6轴训练集"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stderr",
     "text": [
      "D:\\software\\Programming\\anaconda\\lib\\site-packages\\scipy\\signal\\spectral.py:1966: UserWarning: nperseg = 256 is greater than input length  = 38, using nperseg = 38\n  .format(nperseg, input_length))\n",
      "D:\\software\\Programming\\anaconda\\lib\\site-packages\\scipy\\signal\\spectral.py:1966: UserWarning: nperseg = 256 is greater than input length  = 36, using nperseg = 36\n  .format(nperseg, input_length))\n"
     ],
     "output_type": "stream"
    },
    {
     "name": "stdout",
     "text": [
      "特征提取SVM： \n               precision    recall  f1-score   support\n\n           0       1.00      0.90      0.95        20\n           1       0.76      1.00      0.86        19\n           2       1.00      0.94      0.97        18\n           3       1.00      0.82      0.90        17\n\n    accuracy                           0.92        74\n   macro avg       0.94      0.92      0.92        74\nweighted avg       0.94      0.92      0.92        74\n\n滤波6轴SVM： \n               precision    recall  f1-score   support\n\n           0       1.00      1.00      1.00        19\n           1       1.00      1.00      1.00        18\n           2       1.00      1.00      1.00        17\n           3       1.00      1.00      1.00        16\n\n    accuracy                           1.00        70\n   macro avg       1.00      1.00      1.00        70\nweighted avg       1.00      1.00      1.00        70\n\n",
      "特征提取RF：\n              precision    recall  f1-score   support\n\n           0       1.00      1.00      1.00        20\n           1       1.00      1.00      1.00        19\n           2       1.00      0.94      0.97        18\n           3       0.94      1.00      0.97        17\n\n    accuracy                           0.99        74\n   macro avg       0.99      0.99      0.99        74\nweighted avg       0.99      0.99      0.99        74\n\n",
      "滤波6轴RF：\n              precision    recall  f1-score   support\n\n           0       1.00      1.00      1.00        19\n           1       0.90      1.00      0.95        18\n           2       1.00      0.88      0.94        17\n           3       1.00      1.00      1.00        16\n\n    accuracy                           0.97        70\n   macro avg       0.97      0.97      0.97        70\nweighted avg       0.97      0.97      0.97        70\n\n特征提取bayes：\n              precision    recall  f1-score   support\n\n           0       1.00      1.00      1.00        20\n           1       0.86      1.00      0.93        19\n           2       1.00      0.89      0.94        18\n           3       0.94      0.88      0.91        17\n\n    accuracy                           0.95        74\n   macro avg       0.95      0.94      0.94        74\nweighted avg       0.95      0.95      0.95        74\n\n滤波6轴bayes：\n              precision    recall  f1-score   support\n\n           0       1.00      1.00      1.00        19\n           1       0.90      1.00      0.95        18\n           2       1.00      0.88      0.94        17\n           3       1.00      1.00      1.00        16\n\n    accuracy                           0.97        70\n   macro avg       0.97      0.97      0.97        70\nweighted avg       0.97      0.97      0.97        70\n\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "def raw_data(path,time):\n",
    "    data = pd.read_csv(path)\n",
    "    n, m = data.shape\n",
    "    freq = n / time\n",
    "    # block = int(2 * freq // 2 + 1)\n",
    "    block=19\n",
    "    \n",
    "    taccX=window(prefilter(data['accX']),block)\n",
    "    taccY=window(prefilter(data['accY']),block)\n",
    "    taccZ=window(prefilter(data['accZ']),block)\n",
    "    gyrX=window(prefilter(data['geoX']),block)\n",
    "    gyrY=window(prefilter(data['geoY']),block)\n",
    "    gyrZ=window(prefilter(data['geoZ']),block)\n",
    "    # print(taccX)\n",
    "    signals=[]\n",
    "    for i in range(len(taccX)):\n",
    "        signals+=[list(taccX[i])+list(taccY[i])+list(taccZ[i])+list(gyrX[i])+list(gyrY[i])+list(gyrZ[i])]\n",
    "\n",
    "    label = window(data['label'], block)\n",
    "    label = np.array([max(x, key=list(x).count) for x in label])\n",
    "\n",
    "    return np.array(signals), np.array(label)\n",
    "\n",
    "denominator = 10    # changeable 'minimum peak height'\n",
    "X_train, Y_train = extract_features_labels(train_signals, train_labels, trT, trN, trF_s, denominator)\n",
    "X_test, Y_test = extract_features_labels(test_signals, test_labels, tsT, tsN, tsF_s, denominator)\n",
    "X_trainR, Y_trainR = raw_data('HARtrain480s.csv',480)\n",
    "X_testR, Y_testR =raw_data('HARtest80s.csv',80)\n",
    "# print(X_train.shape)\n",
    "\n",
    "stdScaler = StandardScaler().fit(X_train)\n",
    "trainStd = stdScaler.transform(X_train)\n",
    "testStd = stdScaler.transform(X_test)\n",
    "svm = SVC().fit(trainStd,Y_train)\n",
    "target_pred = svm.predict(testStd)\n",
    "print('特征提取SVM：','\\n',\n",
    "      classification_report(Y_test, target_pred))\n",
    "\n",
    "stdScaler2 = StandardScaler().fit(X_trainR)\n",
    "trainStd2 = stdScaler2.transform(X_trainR)\n",
    "testStd2 = stdScaler2.transform(X_testR)\n",
    "svm = SVC().fit(trainStd2,Y_trainR)\n",
    "target_pred2 = svm.predict(testStd2)\n",
    "print('滤波6轴SVM：','\\n',\n",
    "      classification_report(Y_testR, target_pred2))\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=1000)\n",
    "clf.fit(X_train, Y_train)\n",
    "print('特征提取RF：')\n",
    "# print(\"Accuracy on training set is : {}\".format(clf.score(X_train, Y_train)))\n",
    "# print(\"Accuracy on test set is : {}\".format(clf.score(X_test, Y_test)))\n",
    "Y_test_pred = clf.predict(X_test)\n",
    "print(classification_report(Y_test, Y_test_pred))\n",
    "\n",
    "clf2 = RandomForestClassifier(n_estimators=1000)\n",
    "clf2.fit(X_trainR, Y_trainR)\n",
    "print('滤波6轴RF：')\n",
    "# print(\"Accuracy on training set is : {}\".format(clf.score(X_train, Y_train)))\n",
    "# print(\"Accuracy on test set is : {}\".format(clf2.score(X_testR, Y_testR)))\n",
    "Y_test_pred2 = clf2.predict(X_testR)\n",
    "print(classification_report(Y_testR, Y_test_pred2))\n",
    "\n",
    "clf = GaussianNB()\n",
    "clf.fit(X_train, Y_train)\n",
    "Y_test_pred = clf.predict(X_test)\n",
    "print('特征提取bayes：')\n",
    "print(classification_report(Y_test, Y_test_pred))\n",
    "\n",
    "clf2 = GaussianNB()\n",
    "clf2.fit(X_trainR, Y_trainR)\n",
    "Y_test_pred = clf2.predict(X_testR)\n",
    "print('滤波6轴bayes：')\n",
    "print(classification_report(Y_testR, Y_test_pred))\n"
   ],
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  },
  {
   "cell_type": "markdown",
   "source": [
    "复现模型"
   ],
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   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "滤波6轴bayes：\nAccuracy on test set is : 97.14285714285714\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "from math import sqrt\n",
    "from math import exp\n",
    "\n",
    "def separate_by_class(dataset):\n",
    "    separated = dict()\n",
    "    for i in range(len(dataset)):\n",
    "        vector = dataset[i]\n",
    "        class_value = vector[-1]\n",
    "        if (class_value not in separated):\n",
    "            separated[class_value] = list()\n",
    "        separated[class_value].append(vector)\n",
    "    # print('s',len(separated[0]))\n",
    "    return separated\n",
    "\n",
    "def mean(numbers):\n",
    "    return sum(numbers)/float(len(numbers))\n",
    "\n",
    "def stdev(numbers):\n",
    "    avg = mean(numbers)\n",
    "    variance = sum([(x-avg)**2 for x in numbers]) / float(len(numbers)-1)\n",
    "    return sqrt(variance)\n",
    "\n",
    "def summarize_dataset(dataset):\n",
    "    summaries = [(mean(column), stdev(column), len(column)) for column in zip(*dataset)]\n",
    "    del(summaries[-1])\n",
    "    # print(summaries)\n",
    "    return summaries\n",
    "\n",
    "def summarize_by_class(dataset):\n",
    "    separated = separate_by_class(dataset)\n",
    "    summaries = dict()\n",
    "    for class_value, rows in separated.items():\n",
    "        summaries[class_value] = summarize_dataset(rows)\n",
    "    # print(summaries)\n",
    "    return summaries\n",
    "\n",
    "def accuracy_metric(actual, predicted):\n",
    "    correct = 0\n",
    "    for i in range(len(actual)):\n",
    "        if actual[i] == predicted[i]:\n",
    "            correct += 1\n",
    "    return correct / float(len(actual)) * 100.0\n",
    "\n",
    "def calculate_probability(x,mean,stdev):\n",
    "    exponent = exp(-((x-mean)*(x-mean) / (2 * stdev*stdev )))\n",
    "    return (1 / (sqrt(2 * 3.14159265) * stdev)) * exponent\n",
    "\n",
    "def predict(summaries,row):\n",
    "    probability,max_pro=0,0\n",
    "    for i in range(4):\n",
    "        probability=1/4\n",
    "        for j in range(len(row)):\n",
    "            probability=probability*calculate_probability(row[j],summaries[i][j][0],summaries[i][j][1])\n",
    "        # print(probability,max_pro)\n",
    "        if(probability>max_pro):\n",
    "            max_pro=probability\n",
    "            label=i\n",
    "    # print(label)\n",
    "    return label\n",
    "\n",
    "\n",
    "trainframe = pd.DataFrame(X_trainR)\n",
    "trainframe['228']=Y_trainR\n",
    "trainset=trainframe.values\n",
    "summarize=summarize_by_class(trainset)\n",
    "predictions = list()\n",
    "actual=list()\n",
    "for i in range(len(Y_testR)):\n",
    "    output = predict(summarize, X_testR[i])\n",
    "    predictions.append(output)\n",
    "    actual.append(Y_testR[i])\n",
    "    # print(output,Y_test[i])\n",
    "\n",
    "accuracy = accuracy_metric(actual, predictions)\n",
    "print('滤波6轴bayes：')\n",
    "print('Accuracy on test set is :',accuracy)\n",
    "\n",
    "# print('\\nsumarize',summarize)\n"
   ],
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